Cloud features have an important impact on global Navy activities, ranging from the effects of clouds on surface ship and aircraft operations to the limitation and enhancement of surveillance activities. We have been developing an automated system for the recognition of operationally important cloud types using data obtained from the Defense Meteorological Satellite Program (DMSP) satellite system. The satellite is deployed in a sun-synchronous morning orbit, and carries a variety of sensors, including the Optical Line Scanner (OLS). This paper describes ongoing work in the use of physical and textural measures for the automatic recognition of cloud class. We have found the physical measures to be relatively computer intensive, and dependent on visible and IR satellite data. The addition of textural measures shows promise for enhancing the classification capability by using only a single channel and by reducing computer processing time.